“A study at Ball State University’s Center for Business and Economic Research last year found that trade accounted for just 13 percent of America’s lost factory jobs. The vast majority of the lost jobs — 88 percent — were taken by robots and other homegrown factors that reduce factories’ need for human labor.” – AP Canada’s labour force is around 19.6 million people, of which 18.2 million people are employed. Together, they worked something like 2.4 billion hours that month. In December 2016, something like 1.7 million Canadians worked about 240 million hours in manufacturing. Roughly. Because of seasonal adjustments and different data at different times. And error. In terms of our working lives in Canada, collectively, manufacturing is about 10% of[…]
A great mind in public policy told me, just this last September, that people are really bad at judging the rate of technological change and when it’ll affect them. It’s like standing on a railway. You can see the train out there. Some people assume that the train is going to hit them very soon. They get off the tracks. Then, when the train is getting very close, others misjudge the speed and assume that it’s still a far way. And then they get hit. It’s a great analogy because it combines prediction with decision. The rate of technological change is actually quite difficult to predict. If it was easy there’d be a lot more successful startups. One Heuristic Start[…]
Some work is very clearly product work. It’s work on things where the success and failure is dependent on the users of the thing. Your users pay you. Their satisfaction matters above all else. Optimizing for the satisfaction of end users is a distinct activity. Hypotheses have to be assessed and then tested – because it’s very likely that you’re going to be wrong. There’s technology that has to be set up such that it’s reliable and robust for the intermediate to long run. It’s designed to be effective and persistent, with all of the instrumentation that goes along with that. That might include manual A/B testing, user-focused analytics, and extra special attention on the optimization objective. Clear product work is[…]
Why does it seem like all the unimportant, easy stuff gets done first? Look up The Urgency Bias. Employing simplified games and real-life consequential choices, we provide evidence for “urgency bias”, showing that people prefer working on urgent (vs. important) tasks that have shorter (vs. longer) completion window however involving smaller (vs. bigger) outcomes, even when task difficulty, goal gradient, outcome scarcity and task interdependence are held constant.- Zhu, Yeng, Hsee (2014) Even when task difficulty, goal gradient, outcome scarcity AND task interdependence is held constant, urgency wins. Even when it would be more beneficial to do something important instead of something urgent, even when you’re painfully made aware of those incentives, you still gravitate towards doing the urgent. There’s[…]
In general, information retrieval from analytics systems becomes harder with the degree of customization (It gets harder to find things over time). That customization is frequently an expression of the values of a culture over time. The inertia of the technical debt caused by early customization is greater than the inertia of a data driven culture. There are no silver bullets. The rest of this post unpacks that paragraph. Information retrieval from analytics systems becomes harder with the degree of customization Assume a vanilla implementation of Open Web Analytics. Or Google Analytics. Or Adobe Analytics. It’ll tell you a lot about a web system on its own. The optimization objective that is at the core of the business will typically[…]
A lot of what seems to be alarming news on the Virtual Reality front. Steam is reporting a 0.18% penetration on VR headsets. The gadget press is calling it a crashing halt. Or a nose dive. It’s interesting. Let’s reserve judgement. Google Trends suggests a summer lull in interest in VR. What happens in 2017/18 for VR really depends on what happens on the weekend of November 24 – Black Friday. If the demand from early adopters responds the way the supply side wants, we’re in for a roaring year of consolidation in the space. And we’ll be feeling the effects of that consolidation for a very long time. If you’re looking to make a long term bet on a[…]
A data driven culture isn’t necessarily devoid of creativity or imagination. Just the opposite. They’ll have to be especially patient around brand formation. Brands A brand exists in the mind of a person. It usually costs a lot of money for a brand to be impressed upon the cortex of a person. There are certain economies of scale that kick at scale, but still at a considerable cost. If that feels fuzzy, despair not. The framework below is fantastic: Brand and CAC The optimization objective of a startup is valuation. To maximize valuation, Customer Acquisition Cost has to be minimized. As previously explored, nature doesn’t cooperate to keep CAC low. The point of the brand is to reduce CAC[…]
Assume that you’re a founder of a tech startup. Assume that you’ve achieved product-market-solution fit. You’ve nailed it. Time to scale. Many founders are great at sales. But not all founders are great at marketing. And that’s a bit of a problem because of three letters: CAC. The Customer Acquisition Cost CAC is the ratio between dollars spent on marketing, and new customers acquired. And it is related to valuation in a very important way. Let me explain. Take a look at the chart below. This is an output from a standard model of SaaS market penetration. Market size is 333,333 customers, the product will approach saturation at 51% of that target, with a monthly churn rate of 0.20% held[…]
Consider the chart below: There are two series – the total number of cumulative customers (top curve) and the number of new customers added each month (bottom curve). The top curve is shaped like an ‘s’ and the bottom one is shaped like a bell. Each month that goes by, the rate of new customer acquisitions increases up to a point, and then declines. You can see the impact that the bottom curve has on the top, because adding up all the incremental customers yields a cumulative penetration curve. Pop-literature (Moore, Crossing The Chasm) focused on the bell shape of the new customers added curve. Strictly speaking, it’s not a distribution, but the shape causes a degree of comfort with[…]
A digital product will go unloved for years. Somebody new comes into the organization and is tasked with the redesign. Two years and a lot of money, tears, and bruises later, a totally changed product is launched. People hate it. Sometimes the creative force behind the redesign, expecting an avalanche of applause and a Lion, can’t believe that people hate it. Traffic falls. And sometimes people get fired. Usually a few just go away. People are afraid to touch the site. A site goes unloved for years. There’s a binge and purge cycle. It may be the case that not too many people are even that good at managing change at all. Maybe that’s a skill that isn’t too common,[…]